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Neel Jain

Neel Jain has 2 papers in our library, in 2023, most of them on Language models & pretraining; often with John Kirchenbauer, Jonas Geiping, Micah Goldblum.

2 papers in our library · 2023

OpenAlex · h-index 7 · 13 works · 164 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 313 times in our library · 1 more in our library without a page yet · ⭐ Paper Stars ranking: #5000

The profile is the library's match of the name; two people with one name are not always told apart.

Elsewhere: Google Scholar · Semantic Scholar · dblp · ACL Anthology

 Affiliations

🎓University of Maryland, College ParkCollege Park · United States2023–2025 · 5 paperscurrent

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 1 more institution on single bylines are not listed.

 Fields, topics and concepts

Of its works in our library, with the number of works on each.

📚 Shelves 4The field of science this work belongs to (OpenAlex fields).Artificial Intelligence · 2 worksArts and Humanities · 2 worksComputer Science · 2 worksLanguage and Linguistics · 2 works

🧭 Topics 1Research subjects and languages the work is about.Language models & pretraining · 1 work

 Works by year

2020s

20232 papers

 Papers

The most cited first (OpenAlex and our library).

  1. Baseline Defenses for Adversarial Attacks Against Aligned Language Models (2023 · 236 citations)
    Optimization-based jailbreaks, especially the universal, transferable suffix attack (GCG) of Zou et al. (2023), can automatically generate prompts that bypass the safety alignment of chat LLMs. These prompts can be crafted on open models and then used against API-only systems. The authors ask whether standard defense…
  2. Hard Prompts Made Easy: Gradient-Based Discrete Optimization for Prompt Tuning and Discovery (2023 · 141 citations)
    Prompts are the main way to steer large generative models. Readable ("hard") prompts are usually written by hand. Learned continuous ("soft") prompts are opaque, tied to a single model, and unusable through text-only APIs. The authors introduce PEZ, a gradient-based procedure that learns discrete token prompts. It…

All works

3 works of theirs in our library, the most cited first; 2 with a page here, the others link their original until we summarise them.

  1. Baseline Defenses for Adversarial Attacks Against Aligned Language ModelsarXiv:2309.00614Neel Jain, Avi Schwarzschild, Yuxin Wen, et al.2023 · 236 citations
  2. Hard Prompts Made Easy: Gradient-Based Discrete Optimization for Prompt Tuning and DiscoveryConference on Neural Information Processing Systems (NeurIPS)Yuxin Wen, Neel Jain, John Kirchenbauer, et al.2023 · 141 citations
  3. Transformers Can Do Arithmetic with the Right Embeddings ↗arXiv:2405.17399 · no summary yetSean McLeish et al.2024 · 36 citations

 Co-authors here

7 co-authors with a page, the most papers together first.

Venues

Where their works here were published, the most works first, with the editions of those that have a page.

 Cited by

Papers of our library cite their works 313 times (of the 149,439 papers whose bibliographies we read); 78 citing papers with a page here →.

Updated

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